How Marketers Can Leverage Virtual Assistant Apps To Connect

marketing-technology

I decided to understand the evolution of mobile technology and how it may come to redefine how we discover information or rather how information will discover us. I will be honest when I decided to take the challenge I put before myself I was not sure what I would find or how I would come to fulfill it. But I have to admit this journey has taken me beyond the realm of technology and its application to something deeper, something rooted in down-to-earth biological system.

I started at something banal, looking for apps that exist today. And I can say it was quite unsuccessful, I mean what was I to look at – a series of apps (and there aplenty) and present them as my findings? However, it was the seed, the beginning of a discovery that has led me from the quest to find the next big thing to understanding the basis of creating the next big thing. And that is transformative – to my thinking and to my challenge.

Here is how it all began and it all started coming together, one small piece at a time.

I started at the top – searching to find….? But I found a list of what seemed like a disarray of apps and services.

I then re-looked at my assignment and started with recommendation/discovery apps and then progressed to virtual assistant apps.

Recommendation apps – because it is based on personal interest graphs and not just social interactions

Virtual Assistant apps – because it leverages (or is trying to) natural language interface and syncing all activities

Potentially, all the recommendation apps become the resource – a sort of database and the virtual assistant the delivery mechanism.

To summarize quickly based on my learning so far, the way to tie these together for marketers is to:

  1. Collect interest data from recommendation apps (like Foursquare that plans to let brands and merchants access to user data to target them elsewhere)
  2. Synthesize and assimilate information on their consumers through email database, website behavior, social interactions.
  3. Optimize website to be ready for semantic and natural language search for the virtual assistant app adoption.
  4. Target with personalized offers (e.g. user plans to travel from one place to another as on the calendar – instantly sync with an existing app or find the best offer for taxi/car rental options)

But I would like to describe more in detail how I came to the above mentioned.

I looked at some recommendation apps such as Nara, Sosh, Ness 2.0,  and Stamped to see what they captured and how they made decisions for the user (predictive analysis).

For virtual assistant apps, they have been talked about so much; I considered Siri, Sherpa, Osito and Google Now among others.

Together, I think they are attempting to create highly personalized and intelligent answers system.

So what does that mean for not just consumers but also marketers? Does this signify a shift to a more one-to-one recommendation?

Granted the ideas are not altogether new, they have been around for sometime. Especially e-commerce sites have been doing this. Amazon is a great example of the collaborative filtering. Interest based recommendations personalized to my past behavior.

And I think the answer lies in Amazon’s magic and Google’s intelligence with knowledge graph and interest graph.

To reiterate, the recommendation apps/social networks become the resource – a database and the virtual assistant the delivery mechanism. And why do these apps matter? I mean is there not enough data already to provide the interest graph information to marketers?

Yes and no.  To some extent the interest graph information can be found from Facebook, Twitter, YouTube and other social activities. But there is more granularity that these recommendations apps can provide because they are based on the individual’s taste and preference and not just his/her social connections or interactions.

I think there is merit to this approach over using only social recommendations – what my friends like I might like too. The reason I say so and I am still uncovering is that all what my friends like are not necessarily my interests. I think there are more interest-based options on the open web.

More recently, Forrester released their findings on Database of Affinity that proclaimed Google as the winner.

What caught my eye is the fact that Google has more interest signals based on the online activities the users perform including the dark social activities (sharing via email). This together with the collective knowledge of the Internet serves the right answer at the right time. Relevance and interest come together.

For marketers, the implications lie in not only investing in branded experiences but also truly leveraging the technology that these  apps offer.

And more importantly, I think it is no longer just optimizing for keywords on branded sites but also optimizing for answers. It is learning to understand what questions are being asked and how best we can supply with responses. Thinking how Siri or Google Now may eventually bypass links to sites and answer the question – but they will rely on someone to offer those responses.

Enter natural language interface and the theory of Noam Chomsky of how to understand language is learned and processed. It is a little wonder then everyone is after the perfect algorithm. But it goes beyond linguistics, for Artificial Intelligence it is also about motion, gestures, facial recognition and the immediate environment.

Take for instance the Smileage app that is integrated with Google account, knows when you go on a drive, tracks your route, lets you take photos and tag passengers, post it on your behalf. It also pulls in information on the weather, time and events across the web to create a personalized video.

Or have the knowledge that if a consumer who has viewed a recipe on an app, added the ingredients to a shopping list app and her virtual assistant is synced with her activity such as physical grocery shopping to provide this consumer the relevant offer at shelf as she checks of her list.

This presents marketers an opportunity to serve content, targeted ads and offers, based on all these factors.

But it does little if marketers don’t realize the opportunity to becoming experts in their area – optimizing content, tying the data from external sources and meeting the wants of the consumer.

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